Python: Advanced Guide to Artificial Intelligence Expert machine learning systems and intelligent agents using Python
Python: Advanced Guide to Artificial Intelligence: Expert machine learning systems and intelligent agents using Python

bDemystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems/b h4Key Features/h4 ulliMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation /li liBuild deep learning models for object detection, image class...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
1. Verfasser: Bonaccorso, Giuseppe (VerfasserIn)
Format: Elektronisch E-Book
Sprache:English
Veröffentlicht: Birmingham Packt Publishing Limited 2018
Ausgabe:1
Schlagworte:
COMPUTERS / Programming Languages / Python
COMPUTERS / Intelligence (AI) &amp
Semantics
Maschinelles Lernen
Keras > Framework, Informatik
Python > Programmiersprache
TensorFlow
Zusammenfassung:bDemystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems/b h4Key Features/h4 ulliMaster supervised, unsupervised, and semi-supervised ML algorithms and their implementation /li liBuild deep learning models for object detection, image classification, similarity learning, and more /li liBuild, deploy, and scale end-to-end deep neural network models in a production environment /li /ul h4Book Description/h4 This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries.
You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving. You'll implement different techniques related to object classification, object detection, image segmentation, and more.
By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems This Learning Path includes content from the following Packt products: ul liMastering Machine Learning Algorithms by Giuseppe Bonaccorso/li liMastering TensorFlow 1.x by Armando Fandango/li liDeep Learning for Computer Vision by Rajalingappaa Shanmugamani/li /ul h4What you will learn/h4 ulliExplore how an ML model can be trained, optimized, and evaluated /li liWork with Autoencoders and Generative Adversarial Networks /li liExplore the most important Reinforcement Learning techniques /li liBuild end-to-end deep learning (CNN, RNN, and Autoencoders) models/li/ul h4Who this book is for/h4 This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model.
Beschreibung:1 Online-Ressource (764 Seiten)
ISBN:9781789951721

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